This paper provides an objective approach based on available market information capable of reducing subjectivity, inherently present in the process of expected loss provisioning under the IFRS 9.
This paper develops the two-step methodology. Calibrating the Credit Default Swap (CDS)-implied default probabilities to the through-the-cycle default frequencies provides average weights of default component in the spread for each forward term. Then, the impairment provisions are calculated for a sample of investment grade and high yield obligors by distilling their pure default-risk term-structures from the respective term-structures of spreads. This research demonstrates how to estimate credit impairment allowances compliant with IFRS 9 framework.
This study finds that for both investment grade and high yield exposures, the weights of default component in the credit spreads always remain inferior to 33%. The research's outcomes contrast with several previous results stating that the default risk premium accounts at least for 40% of CDS spreads. The proposed methodology is applied to calculate IFRS 9 compliant provisions for a sample of investment grade and high yield obligors.
Many issuers are not covered by individual Bloomberg valuation curves. However, the way to overcome this limitation is proposed.
The proposed approach offers a clue for a better alignment of accounting practices, financial regulation and credit risk management, using expected loss metrics across diverse silos inside organizations. It encourages adopting the proposed methodology, illustrating its application to a set of bond exposures.
No previous research addresses impairment provisioning employing Bloomberg valuation curves. The study fills this gap.
This work was supported by FCT, I.P., the Portuguese national funding agency for science, research and technology, under the Project UIDB/04521/2020, and by Instituto Politécnico de Lisboa as a part of the IPL/2020/MacroRates/ISCAL project.
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